

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
		from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


lowercase			:				Union[str, Any]   =     get_tests_dir("fixtures/test_sentencepiece.model")

lowercase			:				Union[str, Any]   =     {"target_lang": "fi", "source_lang": "en"}
lowercase			:				Any   =     ">>zh<<"
lowercase			:				Dict   =     "Helsinki-NLP/"

if is_torch_available():
		lowercase			:				str   =     "pt"
elif is_tf_available():
		lowercase			:				Optional[int]   =     "tf"
else:
		lowercase			:				Any   =     "jax"







@require_sentencepiece
class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Tuple								=	MarianTokenizer
							UpperCAmelCase_						:		str								=	False
							UpperCAmelCase_						:		Union[str, Any]								=	True




							def       snake_case			(					self						)		->       List[str]:
														super().setUp()
														A      :					Any      		=					['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
														A      :					Dict      		=					dict(zip(__UpperCAmelCase    ,						range(len(__UpperCAmelCase						)						)						)						)
														A      :					List[str]      		=					Path(self.tmpdirname						)
														save_json(__UpperCAmelCase    ,						save_dir / VOCAB_FILES_NAMES['''vocab''']						)
														save_json(__UpperCAmelCase    ,						save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']						)
														if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																					copyfile(__UpperCAmelCase    ,						save_dir / VOCAB_FILES_NAMES['''source_spm''']						)
																					copyfile(__UpperCAmelCase    ,						save_dir / VOCAB_FILES_NAMES['''target_spm''']						)

														A      :					Optional[int]      		=					MarianTokenizer.from_pretrained(self.tmpdirname						)
														tokenizer.save_pretrained(self.tmpdirname						)




							def       snake_case			(					self    ,						**__UpperCAmelCase						)		->       MarianTokenizer:
														return MarianTokenizer.from_pretrained(self.tmpdirname    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       str:
														return (
														    "This is a test",
														    "This is a test",
														)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					List[Any]      		=					'''</s>'''
														A      :					str      		=					0

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase						)    ,						__UpperCAmelCase						)
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase						)    ,						__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Dict      		=					list(self.get_tokenizer().get_vocab().keys()						)

														self.assertEqual(vocab_keys[0]    ,						'''</s>'''						)
														self.assertEqual(vocab_keys[1]    ,						'''<unk>'''						)
														self.assertEqual(vocab_keys[-1]    ,						'''<pad>'''						)
														self.assertEqual(len(__UpperCAmelCase						)    ,						9						)




							def       snake_case			(					self						)		->       Dict:
														self.assertEqual(self.get_tokenizer().vocab_size    ,						9						)




							def       snake_case			(					self						)		->       List[str]:
														A      :					List[Any]      		=					MarianTokenizer.from_pretrained(f'{ORG_NAME}opus-mt-en-de'						)
														A      :					Union[str, Any]      		=					en_de_tokenizer(['''I am a small frog''']    ,						return_tensors=__UpperCAmelCase						)
														self.assertIsInstance(__UpperCAmelCase    ,						__UpperCAmelCase						)
														A      :					Optional[int]      		=					[38, 1_21, 14, 6_97, 3_88_48, 0]
														self.assertListEqual(__UpperCAmelCase    ,						batch.input_ids[0]						)

														A      :					List[Any]      		=					tempfile.mkdtemp()
														en_de_tokenizer.save_pretrained(__UpperCAmelCase						)
														A      :					str      		=					[x.name for x in Path(__UpperCAmelCase						).glob('''*'''						)]
														self.assertIn('''source.spm'''    ,						__UpperCAmelCase						)
														MarianTokenizer.from_pretrained(__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					List[str]      		=					self.get_tokenizer()

														A      :					Any      		=					tok(
														    ['''I am a small frog''' * 10_00, '''I am a small frog''']    ,						padding=__UpperCAmelCase    ,						truncation=__UpperCAmelCase    ,						return_tensors=__UpperCAmelCase						)
														self.assertIsInstance(__UpperCAmelCase    ,						__UpperCAmelCase						)
														self.assertEqual(batch.input_ids.shape    ,						(2, 5_12)						)




							def       snake_case			(					self						)		->       Tuple:
														A      :					Optional[int]      		=					self.get_tokenizer()
														A      :					Union[str, Any]      		=					tok(['''I am a tiny frog''', '''I am a small frog''']    ,						padding=__UpperCAmelCase    ,						return_tensors=__UpperCAmelCase						)
														self.assertIsInstance(__UpperCAmelCase    ,						__UpperCAmelCase						)
														self.assertEqual(batch_smaller.input_ids.shape    ,						(2, 10)						)




							@slow
							def       snake_case			(					self						)		->       Optional[Any]:
														# fmt: off
														A      :					Tuple      		=					{'''input_ids''': [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
														# fmt: on

														self.tokenizer_integration_test_util(
														    expected_encoding=__UpperCAmelCase    ,						model_name='''Helsinki-NLP/opus-mt-en-de'''    ,						revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''    ,						decode_kwargs={'''use_source_tokenizer''': True}    ,						)






							def       snake_case			(					self						)		->       Tuple:
														A      :					str      		=					MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''						)

														A      :					str      		=					'''Tämä on testi'''
														A      :					int      		=					'''This is a test'''

														A      :					str      		=					[76, 7, 20_47, 2]
														A      :					List[str]      		=					[69, 12, 11, 9_40, 2]

														A      :					Optional[Any]      		=					tokenizer(__UpperCAmelCase						).input_ids
														self.assertListEqual(__UpperCAmelCase    ,						__UpperCAmelCase						)

														A      :					List[Any]      		=					tokenizer(text_target=__UpperCAmelCase						).input_ids
														self.assertListEqual(__UpperCAmelCase    ,						__UpperCAmelCase						)

														A      :					Dict      		=					tokenizer.decode(__UpperCAmelCase    ,						skip_special_tokens=__UpperCAmelCase						)
														self.assertEqual(__UpperCAmelCase    ,						__UpperCAmelCase						)









import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														self.register_modules(vqvae=__UpperCAmelCase    ,						unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)






							@torch.no_grad()
							def __call__(					self    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = 50    ,						__UpperCAmelCase = "pil"    ,						__UpperCAmelCase = True    ,						**__UpperCAmelCase    ,						)		->       Union[Tuple, ImagePipelineOutput]:
														A      :					List[Any]      		=					randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,						generator=__UpperCAmelCase    ,						)
														A      :					List[Any]      		=					latents.to(self.device						)

														# scale the initial noise by the standard deviation required by the scheduler
														A      :					Optional[int]      		=					latents * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(__UpperCAmelCase						)

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														A      :					str      		=					'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

														A      :					List[str]      		=					{}
														if accepts_eta:
																					A      :					Optional[Any]      		=					eta

														for t in self.progress_bar(self.scheduler.timesteps						):
																					A      :					Any      		=					self.scheduler.scale_model_input(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					# predict the noise residual
																					A      :					Dict      		=					self.unet(__UpperCAmelCase    ,						__UpperCAmelCase						).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					A      :					int      		=					self.scheduler.step(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						).prev_sample

														# decode the image latents with the VAE
														A      :					str      		=					self.vqvae.decode(__UpperCAmelCase						).sample

														A      :					Tuple      		=					(image / 2 + 0.5).clamp(0    ,						1						)
														A      :					int      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
														if output_type == "pil":
																					A      :					Union[str, Any]      		=					self.numpy_to_pil(__UpperCAmelCase						)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__UpperCAmelCase						)






